The Physician Anesthesia Workforce in Canada From 1996 to 2018: A Longitudinal Analysis of Health Administrative Data
Bibliographic record
Abstract
BACKGROUND: A robust anesthesia workforce is essential to the provision of safe surgical, obstetrical, and critical care but information describing the physician anesthesia workforce and volume of clinical services delivered in Canada is limited. This study examines the Canadian physician anesthesia workforce, exploring trends in physician characteristics and activity levels over time. Practice patterns of specialist anesthesiologists and family physician anesthetists (FPAs) working in urban and rural communities were of particular interest. METHODS: Physicians who provided anesthesia care between 1996 and 2018 were identified using health administrative data from the Canadian Institute of Health Information (CIHI). In addition, data from the Canadian Post-MD Education Registry (CAPER) were used to characterize physicians pursuing postgraduate anesthesia training (1996-2019). Descriptive analyses of physician demographics, training, location, specialty designations, and volume of clinical services were undertaken. RESULTS: Between 1996 and 2018, the anesthesia workforce grew 1.8-fold to 3681 physicians, including 536 FPAs. Over the same time, nerve block services increased 7-fold, and payments for other anesthesia services increased 5-fold. The average age of the anesthesiology workforce increased by 2.3 years and the annual retirement rate was 3%. The workforce has become more gender balanced but remains predominantly male (73% in 2018). The proportion of physicians who were trained internationally (about 30%; 38% in rural areas) remained stable (and higher than that in the overall physician workforce). FPAs provided most anesthesia care in rural Canada and their attrition rate was generally 2- to 3-fold higher than specialists. Physicians in the rural anesthesia workforce provided anesthesia services more intensively over time. Relatively few FPAs who left the anesthesia workforce entered full retirement and they instead contributed other medical services to their communities. CONCLUSIONS: This study provides foundational information regarding anesthesia workforce capacity over a 22-year period, including insights into demographics, locations of practice, and clinical volumes. The results do not quantify the gap between service capacity and need; however, they support the need for a national workforce strategy to achieve equitable access to sustainable anesthesia services in Canada, particularly for rural communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".